Multiple-antenna systems is a key technique for serving multiple users in future wireless systems. For low energy consumption and hardware complexity we first consider transmit symbols with constant magnitude and then 1-bit digital-to-analog converters (DACs). We propose precoding designs which maximize the minimum distance to the decision threshold at the receiver. The precoding design with 1-bit DAC corresponds to a discrete optimization problem, which we solve exactly with a branch-and-bound strategy. We alternatively present an approximation based on relaxation. Our results show that the proposed branch-and-bound approach has polynomial complexity. The proposed methods outperform existing precoding methods with 1-bit DAC in terms of uncoded bit error rate and sum-rate. The performance loss in comparison to infinite DAC resolution is small.

We present a novel iterative detection and decoding scheme for uplink large-scale multiuser multiple-antenna systems. In order to reduce the receiver's energy consumption and computational complexity, 1-bit analog-to-digital converters are used in the front-end. The performance loss due to the 1-bit quantization can be mitigated by using large-scale antenna arrays. We propose a linear low-resolution-aware minimum mean square error detector for soft multiuser interference mitigation. Moreover, short block length low-density parity-check codes are considered for avoiding high latency. In the channel decoder, a quasi-uniform quantizer with scaling factors is devised to lower the error floor of low-density parity-check codes. Simulations show good performance of the system in terms of bit error rate as compared to prior work.

To ensure signal receiving quality, robust adaptive beamforming (RAB) is of vital importance in modern communications. In this letter, we propose a new low-complexity RAB approach based on interference-plus-noise covariance matrix (IPNC) reconstruction and steering vector (SV) estimation. In this method, the IPNC and desired signal covariance matrices are reconstructed by estimating all interference powers as well as the desired signal power using the principle of maximum entropy power spectrum (MEPS). Numerical simulations demonstrate that the proposed method can provide superior performance to several previously proposed beamformers.

This paper develops practical linear precoding and stream combining techniques for rate-splitting (RS) in multiuser multiple-input multiple-output (MIMO) systems. We propose linear combining techniques based on the Min-Max, the maximum ratio and the minimum mean-square error criteria along with Regularized Block Diagonalization (RBD) precoders for RS-based multiuser multiple-antenna systems. An analysis of the sum rate performance is carried out, leading to closed-form expressions. Simulations show that the proposed combining schemes offer a significant sum rate performance gain over conventional linear precoding schemes.

In this paper, we propose an adaptive linear-receiver structure based on interpolated finite-impulse response (FIR) filters with adaptive interpolators for direct-sequence code-division multiple-access (DS-CDMA) systems in multipath channels. The interpolated minimum mean-squared error (MMSE) and the interpolated constrained minimum-variance (CMV) solutions are described for a novel scheme, where the interpolator is rendered time-varying in order to mitigate multiple-access interference and multiple-path propagation effects. Based upon the interpolated MMSE and CMV solutions, we present computationally efficient stochastic gradient and exponentially weighted recursive least squares type algorithms for both receiver and interpolator filters in the supervised and blind modes of operation. A convergence analysis of the algorithms and a discussion of the convergence properties of the method are carried out for both modes of operation. Simulation experiments for a downlink scenario show that the proposed structures achieve a superior bit-error-rate convergence and steady-state performance to previously reported reduced-rank receivers at lower complexity.

This work develops robust diffusion recursive least squares algorithms to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. The first algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent constraint on the squared norm of the intermediate update at each node. A recursive strategy for computing the constraint is proposed using side information from the neighboring nodes to further improve the robustness. We also analyze the mean-square convergence behavior of the proposed algorithm. The second proposed algorithm is a modification of the first one based on the dichotomous coordinate descent iterations. It has a performance similar to that of the former, however its complexity is significantly lower especially when input regressors of agents have a shift structure and it is well suited to practical implementation. Simulations show the superiority of the proposed algorithms over previously reported techniques in various impulsive noise scenarios.